Department of MathematicsDepartment of Computer Engineering2024-11-092006978-1-4244-0354-71550-3607N/A2-s2.0-42549105614N/Ahttps://hdl.handle.net/20.500.14288/12652We compare pull and push-based epidemic paradigms for information diffusion in large scale networks. Key benefits of these approaches are that they are fully distributed, utilize local information only via pair-wise interactions, and provide eventual consistency, scalability and communication topology-independence, which make them suitable for peer-to-peer distributed systems. We develop a chain-Binomial epidemic probability model for these algorithms. Our main contribution is the exact computation of message delivery latency observed by each peer, which corresponds to a first passage time of the underlying Markov chain. Such an analytical tool facilitates the comparison of pull and push-based spread for different group sizes, initial number of infectious peers and fan-out values which are also accomplished in this study. Via our analytical stochastic model, we show that push-based approach is expected to facilitate faster information spread both for the whole group and as experienced by each member.Computer science, hardware and architectureEngineering, electrical and electronicTelecommunicationsConference proceeding287032701003N/A12854